Generally, improvements in radar system configurations have focused on performance characteristics such as greater range, higher efficiency, or new functional modes and capabilities. Over most of the history of radar, typical examples of such improvements have been better power management, greater effective radiated power, lower noise, greater stability, or specialized modulation and beam forming techniques. Additionally component integration (such as MIMIC devices and integrated T/R modules) and miniaturization of mechanical and electronic components within the system have improved system reliability and performance. However, system configurations and integrated system architectures have typically remained the same over many system generations—a by-product of an evolutionary design approach.
More recently, as digital technology has developed, software control and software radar receivers have enabled dynamic reconfigurability and more flexible, computationally intensive data analysis. Conversion of traditional analog implementation structure to digital devices and radar implementations have led to software selectable operation modes, e.g., target detection, target tracking (Track), moving target indicator (MTI), and others. Furthermore, the migration from entirely analog to mostly digital systems has made possible complex modes of operation such as synthetic aperture imaging (SAR) and other computationally intensive radar applications.
The advent of affordable, large scale, fast computer data management, embedded digital signal processors (DSPs), and affordable, fast data storage has spawned new concepts in data transfer and data processing for radar systems. An example of such a radar system organization is the ‘scalable radar signal processing system’ described by R. Gaentgen, (U.S. Pat. No. 6,545,635, issued Apr. 8, 2003), wherein one or more digital signal processing units are connected in a parallel fashion to an information transfer bus. Furthermore, organizational structures have been developed for computers, mass data storage, and for networks of computers and storage devices that effect data transfer rates in the gigabit per second (Gbps) range, multiple user accessible storage area networks, and computer clusters. These have direct applicability to radar systems that employ digital processing and ‘software receivers’. An example of such a computer development is the ‘loosely coupled mass storage computer cluster’ described by B. E. Mann et al. (U.S. Pat. No. 6,557,114, issued Apr. 29, 2003). Still further, networks of computers, sensors, and instruments are well known, e.g., the internet concept, as is the use of a network within a localized system embodied as an intranet or as a parallel bus structure.
In the prior art for radar systems, computer systems, and networks, however, each system comprises subsystem units that are relatively complex, multi-component assemblies, and these subunits are connected as subordinate clients on a data transfer system. Although some of these subunits may have their own embedded processors and data storage, it has not been recognized that each subunit may be organized as a structure that has similarity with the overall system and also, that its subunits may also be organized as such similar units. This kind of multi-layered system organization is referred to as ‘self-similar’ because the overall system and its individual layers share a similar organizational structure. The ranking of subordinate layers of system subunits is called a ‘hierarchical’ scheme. Because of the ranking, the layers are also referred to as levels. The uppermost level can be assigned to the overall system, the global level. The lowest level can be assigned to the ‘component’ or ‘device’ level.